wikilangs/kcg

Tyap - Wikilangs Models

Comprehensive Research Report & Full Ablation Study

This repository contains NLP models trained and evaluated by Wikilangs, specifically on Tyap Wikipedia data. We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.

📋 Repository Contents

Models & Assets

• Tokenizers (8k, 16k, 32k, 64k)
• N-gram models (2, 3, 4, 5-gram)
• Markov chains (context of 1, 2, 3, 4 and 5)
• Subword N-gram and Markov chains
• Embeddings in various sizes and dimensions (aligned and unaligned)
• Language Vocabulary
• Language Statistics

Analysis and Evaluation

• 1. Tokenizer Evaluation
• 2. N-gram Model Evaluation
• 3. Markov Chain Evaluation
• 4. Vocabulary Analysis
• 5. Word Embeddings Evaluation
• 6. Morphological Analysis (Experimental)
• 7. Summary & Recommendations
• Metrics Glossary
• Visualizations Index

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1. Tokenizer Evaluation

Results

Vocab Size	Compression	Avg Token Len	UNK Rate	Total Tokens
8k	4.149x	4.15	0.1551%	192,111
16k	4.452x	4.46	0.1664%	179,058
32k	4.706x	4.71	0.1760%	169,365
64k	4.834x 🏆	4.84	0.1807%	164,911

Tokenization Examples

Below are sample sentences tokenized with each vocabulary size:

Sample 1: Atanii yet mam hwa kunin kyak avwou mun tsatsak ladi mang talata . Wikimedians Z...

Vocab	Tokens	Count
8k	▁at ani i ▁yet ▁mam ▁hwa ▁ku nin ▁kyak ▁avwo ... (+11 more)	21
16k	▁atanii ▁yet ▁mam ▁hwa ▁ku nin ▁kyak ▁avwou ▁mun ▁tsatsak ... (+8 more)	18
32k	▁atanii ▁yet ▁mam ▁hwa ▁kunin ▁kyak ▁avwou ▁mun ▁tsatsak ▁ladi ... (+6 more)	16
64k	▁atanii ▁yet ▁mam ▁hwa ▁kunin ▁kyak ▁avwou ▁mun ▁tsatsak ▁ladi ... (+6 more)	16

Sample 2: Zong (á̱ ka ndyuut zwong a̱ni) yet jen nang a̱yin nswan a̱fa a̱khwot di̱ mi̱n ya...

Vocab	Tokens	Count
8k	▁zong ▁( á̱ ▁ka ▁ndyuut ▁z wong ▁a̱ni ) ▁yet ... (+19 more)	29
16k	▁zong ▁( á̱ ▁ka ▁ndyuut ▁z wong ▁a̱ni ) ▁yet ... (+19 more)	29
32k	▁zong ▁( á̱ ▁ka ▁ndyuut ▁z wong ▁a̱ni ) ▁yet ... (+19 more)	29
64k	▁zong ▁( á̱ ▁ka ▁ndyuut ▁zwong ▁a̱ni ) ▁yet ▁jen ... (+18 more)	28

Sample 3: Ci̱ncai yet a̱cyuang ga̱swan ba̱ ya ka̱tako a̱ni. Ya̱fang

Vocab	Tokens	Count
8k	▁c i̱n c ai ▁yet ▁a̱cyuang ▁ga̱s wan ▁ba̱ ▁ya ... (+5 more)	15
16k	▁c i̱n c ai ▁yet ▁a̱cyuang ▁ga̱swan ▁ba̱ ▁ya ▁ka̱tak ... (+4 more)	14
32k	▁ci̱ncai ▁yet ▁a̱cyuang ▁ga̱swan ▁ba̱ ▁ya ▁ka̱tako ▁a̱ni . ▁ya̱fang	10
64k	▁ci̱ncai ▁yet ▁a̱cyuang ▁ga̱swan ▁ba̱ ▁ya ▁ka̱tako ▁a̱ni . ▁ya̱fang	10

Key Findings

• Best Compression: 64k achieves 4.834x compression
• Lowest UNK Rate: 8k with 0.1551% unknown tokens
• Trade-off: Larger vocabularies improve compression but increase model size
• Recommendation: 32k vocabulary provides optimal balance for production use

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2. N-gram Model Evaluation

Results

N-gram	Variant	Perplexity	Entropy	Unique N-grams	Top-100 Coverage	Top-1000 Coverage
2-gram	Word	2,665	11.38	5,715	23.5%	60.8%
2-gram	Subword	265 🏆	8.05	1,919	66.6%	99.3%
3-gram	Word	3,873	11.92	6,453	18.1%	48.8%
3-gram	Subword	1,877	10.87	12,850	30.0%	74.6%
4-gram	Word	6,271	12.61	8,735	12.5%	36.0%
4-gram	Subword	8,185	13.00	52,350	17.1%	47.5%
5-gram	Word	3,808	11.89	4,846	13.7%	41.8%
5-gram	Subword	20,242	14.31	96,936	11.3%	34.0%

Top 5 N-grams by Size

2-grams (Word):

Rank	N-gram	Count
1	nang á̱	1,002
2	di̱ fam	924
3	á̱ ku	675
4	a̱ si̱	657
5	ku yet	653

3-grams (Word):

Rank	N-gram	Count
1	di̱ fam a̱tak	234
2	nang á̱ ku	230
3	di̱ fam a̱za	209
4	nang á̱ ngyei	200
5	ya̱fang a̱ka̱fwuop nta	196

4-grams (Word):

Rank	N-gram	Count
1	zwat swak ma̱ng sweang	86
2	kyiak neet ma̱ a̱lyia̱	82
3	wiki bootcamp season 1	80
4	di̱ fam a̱za hu	72
5	di̱ fam a̱tyin hu	70

5-grams (Word):

Rank	N-gram	Count
1	neet ma̱ a̱lyia̱ ba̱ng si̱	62
2	á̱ lyen ma̱ng a̱lyoot a̱gwomna̱ti	62
3	kyiak neet ma̱ a̱lyia̱ ba̱ng	59
4	in tyap romanian and english	58
5	together in tyap romanian and	58

2-grams (Subword):

Rank	N-gram	Count
1	_ a̱	38,395
2	n g	35,424
3	a n	31,339
4	t _	27,103
5	a _	26,601

3-grams (Subword):

Rank	N-gram	Count
1	n g _	26,180
2	a n g	17,304
3	e t _	10,561
4	_ m a̱	8,983
5	a t _	7,766

4-grams (Subword):

Rank	N-gram	Count
1	a n g _	13,963
2	y i a̱ _	6,492
3	a̱ n g _	6,360
4	_ m a̱ n	6,098
5	m a̱ n g	5,713

5-grams (Subword):

Rank	N-gram	Count
1	m a̱ n g _	5,692
2	_ m a̱ n g	5,676
3	_ y e t _	4,648
4	n a n g _	3,924
5	b y i n _	3,628

Key Findings

• Best Perplexity: 2-gram (subword) with 265
• Entropy Trend: Decreases with larger n-grams (more predictable)
• Coverage: Top-1000 patterns cover ~34% of corpus
• Recommendation: 4-gram or 5-gram for best predictive performance

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3. Markov Chain Evaluation

Results

Context	Variant	Avg Entropy	Perplexity	Branching Factor	Unique Contexts	Predictability
1	Word	0.7557	1.688	4.71	28,147	24.4%
1	Subword	0.9793	1.972	6.49	911	2.1%
2	Word	0.2473	1.187	1.54	132,079	75.3%
2	Subword	0.8642	1.820	4.83	5,908	13.6%
3	Word	0.0833	1.059	1.13	202,426	91.7%
3	Subword	0.7719	1.708	3.49	28,551	22.8%
4	Word	0.0300 🏆	1.021	1.04	228,145	97.0%
4	Subword	0.5638	1.478	2.31	99,668	43.6%

Generated Text Samples (Word-based)

Below are text samples generated from each word-based Markov chain model:

Context Size 1:

1. ma̱ng a̱lyoot a̱liza̱nda mi̱ a̱bibyia̱ njen nang si̱tet ba̱yelsa shyia̱ cet a̱gwaza a̱nyiung di̱ fam...
2. ku nihon shong kaswuo a̱ni nggu a̱tyoli sa̱mwila a̱cyia̱ shong mediterranean ba̱ nyia̱ a̱yaafim ku s...
3. yet a̱tyulyuut ma̱ng a̱za jenshyung si̱tet ka̱duna a̱tak shong www stoa org dead keys in the

Context Size 2:

1. nang á̱ ku mbwuo lyulyoot a̱ni ni̱nia yet guadalajara monterrey puebla toluca tijuana ciudad juárez ...
2. di̱ fam a̱byin jenshyung a̱siya a̱sa̱khwot nhu na a̱ni tamah si̱ ci a̱pyie ngu nang kham nsaai
3. á̱ ku mi̱n a̱ khwuat a̱nietca̱tshot a̱niet khwo mba tai a̱ ku ngyei gini potuga a̱ni ma̱nang

Context Size 3:

1. di̱ fam a̱tak hu a̱za afrika si̱ myian a̱ja a̱wot di̱ fam a̱tak si̱tet ka̱duna naijeriya a̱ nyia̱
2. nang á̱ ku byin nggu a̱tali̱gan a̱ga̱mi tshshekari was born in taligan magamia zangon kataf to paren...
3. di̱ fam a̱za hu naat kyai a̱sa̱khwot caina a̱tak hu yet kyai a̱sa̱khwot ku shyia̱ di̱ ngaan fam

Context Size 4:

1. zwat swak ma̱ng sweang yet a̱tyukwai nfwuo á̱niet naijeriya wa a̱nyan wa yet byiek a̱kwak a̱son á̱gw...
2. kyiak neet ma̱ a̱lyia̱ ba̱ng si̱ tat a̱ ku ba̱ng cucuk a̱gwomna̱ti jhyang di̱n jen ji̱ ku swak a̱ni
3. di̱ fam a̱za hu a̱fganistan di̱ fam a̱tyin hu ka̱ ka̱u di̱ si̱sak nang lili a̱byin ka yet a̱ni

Generated Text Samples (Subword-based)

Below are text samples generated from each subword-based Markov chain model:

Context Size 1:

1. _á̱_fabefandera_e
2. anwu.,_hwunre,_m
3. ngbamang_mi̱ta_á̱k

Context Size 2:

1. _a̱fangba̱_ny-fwuo_
2. ng_hi_biya_bya_si̱
3. ang_á̱ni._ya̱u_vin_

Context Size 3:

1. ng_a̱yaaethe_part_o
2. angka̱i_a̱khai_ba_,_
3. et_a̱lyen_shong_a̱ku

Context Size 4:

1. ang_gini_ka̱sitibin_
2. yia̱_a̱yaapi̱rotidia._
3. a̱ng_si̱_swak_mi̱_suso

Key Findings

• Best Predictability: Context-4 (word) with 97.0% predictability
• Branching Factor: Decreases with context size (more deterministic)
• Memory Trade-off: Larger contexts require more storage (99,668 contexts)
• Recommendation: Context-3 or Context-4 for text generation

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4. Vocabulary Analysis

Statistics

Metric	Value
Vocabulary Size	11,223
Total Tokens	236,752
Mean Frequency	21.10
Median Frequency	3
Frequency Std Dev	149.81

Most Common Words

Rank	Word	Frequency
1	ma̱ng	5,701
2	ku	5,107
3	yet	4,705
4	si̱	3,684
5	a̱ni	3,615
6	hu	3,553
7	á̱	3,391
8	nang	3,386
9	a̱	3,096
10	ka	2,820

Least Common Words (from vocabulary)

Rank	Word	Frequency
1	tockus	2
2	erythrorhynchus	2
3	atu	2
4	luwut	2
5	akad	2
6	أبو	2
7	نواس	2
8	nuwās	2
9	a̱tyoka̱u	2
10	basi̱li̱kata	2

Zipf's Law Analysis

Metric	Value
Zipf Coefficient	1.1596
R² (Goodness of Fit)	0.992895
Adherence Quality	excellent

Coverage Analysis

Top N Words	Coverage
Top 100	48.0%
Top 1,000	78.4%
Top 5,000	93.6%
Top 10,000	99.0%

Key Findings

• Zipf Compliance: R²=0.9929 indicates excellent adherence to Zipf's law
• High Frequency Dominance: Top 100 words cover 48.0% of corpus
• Long Tail: 1,223 words needed for remaining 1.0% coverage

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5. Word Embeddings Evaluation

5.1 Cross-Lingual Alignment

5.2 Model Comparison

Model	Dimension	Isotropy	Semantic Density	Alignment R@1	Alignment R@10
mono_32d	32	0.3873 🏆	0.4467	N/A	N/A
mono_64d	64	0.0916	0.4260	N/A	N/A
mono_128d	128	0.0123	0.4367	N/A	N/A
aligned_32d	32	0.3873	0.4319	0.0240	0.1440
aligned_64d	64	0.0916	0.4421	0.0200	0.1440
aligned_128d	128	0.0123	0.4376	0.0160	0.1340

Key Findings

• Best Isotropy: mono_32d with 0.3873 (more uniform distribution)
• Semantic Density: Average pairwise similarity of 0.4368. Lower values indicate better semantic separation.
• Alignment Quality: Aligned models achieve up to 2.4% R@1 in cross-lingual retrieval.
• Recommendation: 128d aligned for best cross-lingual performance

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6. Morphological Analysis (Experimental)

This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.

6.1 Productivity & Complexity

Metric	Value	Interpretation	Recommendation
Productivity Index	5.000	High morphological productivity	Reliable analysis
Idiomaticity Gap	0.229	High formulaic/idiomatic content	-

6.2 Affix Inventory (Productive Units)

These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.

Productive Prefixes

Prefix	Examples
-a	a̱tyuweang, a̱ka̱satyok, american
-n	nia, ning, na̠
-s	sardi, songs, swot
-m	ma̱m, ma̱li̱daviya, mabyin
-k	kwaimam, kpantyin, kwom
-b	bendel, bu, buzău
-t	tyantung, ta̱lyi̱ri̱p, tunis
-ma	ma̱m, ma̱li̱daviya, mabyin

Productive Suffixes

Suffix	Examples
-a	nia, ania, ma̱li̱daviya
-n	american, a̱yangka̱nan, rénmín
-ng	ga̱swúong, a̱tyuweang, tyantung
-t	lilyuut, felt, list
-g	ga̱swúong, a̱tyuweang, tyantung
-i	yhui, a̱yaazoni, a̱vwui
-s	prayers, songs, français
-e	fare, harare, senate

6.3 Bound Stems (Lexical Roots)

Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.

Stem	Cohesion	Substitutability	Examples
yang	1.38x	56 contexts	gyang, lyang, jyang
wang	1.62x	25 contexts	gwang, nwang, swang
eang	1.59x	26 contexts	keang, weang, teang
tion	1.88x	13 contexts	action, nation, notion
wuan	1.50x	23 contexts	swuan, fwuan, vwuan
yiak	1.67x	16 contexts	tyiak, kyiak, byiak
yiat	1.56x	18 contexts	tyiat, lyiat, kyiat
wuon	1.51x	19 contexts	fwuon, vwuon, bwuon
hyan	1.69x	11 contexts	nhyan, ghyang, hihyan
nshy	1.33x	14 contexts	nshye, nshya, nshyie
kean	1.50x	9 contexts	keang, keana, a̱kean
nyiu	1.48x	9 contexts	a̱nyiu, nyiung, anyiuk

6.4 Affix Compatibility (Co-occurrence)

This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.

Prefix	Suffix	Frequency	Examples
-a	-g	194 words	anbang, a̱tyubwuanng
-a	-ng	193 words	anbang, a̱tyubwuanng
-a	-t	166 words	a̱gwut, a̱tat
-a	-i	144 words	a̱ta̱nii, agwii
-a	-a	137 words	alata, a̱jiya
-a	-n	131 words	afwun, a̱zabyin
-a	-k	104 words	acucuk, akanok
-a	-an	53 words	ashan, american
-c	-s	41 words	collins, caucasus
-k	-a	41 words	kola, ki̱risi̱ta

6.5 Recursive Morpheme Segmentation

Using Recursive Hierarchical Substitutability, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., prefix-prefix-root-suffix).

Word	Suggested Split	Confidence	Stem
marketing	market-i-ng	7.5	i
kuzangmam	kuzang-m-am	7.5	m
a̱ka̱safang	a̱ka̱saf-a-ng	7.5	a
kyangtutu	kyangtu-t-u	7.5	t
ka̱zaktan	ka̱zak-t-an	7.5	t
á̱nietnzop	á̱nietnz-o-p	7.5	o
christians	christi-an-s	7.5	an
atakjenshyung	at-ak-jenshyung	7.5	jenshyung
nvwuomaat	nvwuom-a-at	7.5	a
institution	institut-i-on	7.5	i
a̱tyulyiai	a̱tyuly-i-ai	7.5	i
nggwoneam	nggwon-e-am	7.5	e
a̱nyanyan	a̱nyan-ya-n	6.0	a̱nyan
africaines	africa-in-es	6.0	africa
a̱kwokwak	a̱kwok-wa-k	6.0	a̱kwok

6.6 Linguistic Interpretation

Automated Insight: The language Tyap shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.

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7. Summary & Recommendations

Production Recommendations

Component	Recommended	Rationale
Tokenizer	64k BPE	Best compression (4.83x)
N-gram	2-gram	Lowest perplexity (265)
Markov	Context-4	Highest predictability (97.0%)
Embeddings	100d	Balanced semantic capture and isotropy

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Appendix: Metrics Glossary & Interpretation Guide

This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.

Tokenizer Metrics

Compression Ratio

Definition: The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.

Intuition: Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.

What to seek: Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.

Average Token Length (Fertility)

Definition: Mean number of characters per token produced by the tokenizer.

Intuition: Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.

What to seek: Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.

Unknown Token Rate (OOV Rate)

Definition: Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.

Intuition: Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.

What to seek: Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.

N-gram Model Metrics

Perplexity

Definition: Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.

Intuition: If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.

What to seek: Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.

Entropy

Definition: Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.

Intuition: High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.

What to seek: Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.

Coverage (Top-K)

Definition: Percentage of corpus occurrences explained by the top K most frequent n-grams.

Intuition: High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.

What to seek: Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.

Markov Chain Metrics

Average Entropy

Definition: Mean entropy across all contexts, measuring average uncertainty in next-word prediction.

Intuition: Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).

What to seek: Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.

Branching Factor

Definition: Average number of unique next tokens observed for each context.

Intuition: High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).

What to seek: Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.

Predictability

Definition: Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.

Intuition: 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.

What to seek: Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.

Vocabulary & Zipf's Law Metrics

Zipf's Coefficient

Definition: The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.

Intuition: A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.

What to seek: Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.

R² (Coefficient of Determination)

Definition: Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.

Intuition: R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.

What to seek: R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.

Vocabulary Coverage

Definition: Cumulative percentage of corpus tokens accounted for by the top N words.

Intuition: Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.

What to seek: Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.

Word Embedding Metrics

Isotropy

Definition: Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.

Intuition: High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.

What to seek: Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.

Average Norm

Definition: Mean magnitude (L2 norm) of word vectors in the embedding space.

Intuition: Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.

What to seek: Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).

Cosine Similarity

Definition: Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).

Intuition: Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.

What to seek: Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.

t-SNE Visualization

Definition: t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.

Intuition: Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.

What to seek: Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.

General Interpretation Guidelines

1. Compare within model families: Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
2. Consider trade-offs: Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
3. Context matters: Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
4. Corpus influence: All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
5. Language-specific patterns: Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.

Visualizations Index

Visualization	Description
Tokenizer Compression	Compression ratios by vocabulary size
Tokenizer Fertility	Average token length by vocabulary
Tokenizer OOV	Unknown token rates
Tokenizer Total Tokens	Total tokens by vocabulary
N-gram Perplexity	Perplexity by n-gram size
N-gram Entropy	Entropy by n-gram size
N-gram Coverage	Top pattern coverage
N-gram Unique	Unique n-gram counts
Markov Entropy	Entropy by context size
Markov Branching	Branching factor by context
Markov Contexts	Unique context counts
Zipf's Law	Frequency-rank distribution with fit
Vocab Frequency	Word frequency distribution
Top 20 Words	Most frequent words
Vocab Coverage	Cumulative coverage curve
Embedding Isotropy	Vector space uniformity
Embedding Norms	Vector magnitude distribution
Embedding Similarity	Word similarity heatmap
Nearest Neighbors	Similar words for key terms
t-SNE Words	2D word embedding visualization
t-SNE Sentences	2D sentence embedding visualization
Position Encoding	Encoding method comparison
Model Sizes	Storage requirements
Performance Dashboard	Comprehensive performance overview

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About This Project

Data Source

Models trained on wikipedia-monthly - a monthly snapshot of Wikipedia articles across 300+ languages.

Project

A project by Wikilangs - Open-source NLP models for every Wikipedia language.

Maintainer

Omar Kamali - Omneity Labs

Citation

If you use these models in your research, please cite:

@misc{wikilangs2025,
  author = {Kamali, Omar},
  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
  year = {2025},
  doi = {10.5281/zenodo.18073153},
  publisher = {Zenodo},
  url = {https://huggingface.co/wikilangs}
  institution = {Omneity Labs}
}

License

MIT License - Free for academic and commercial use.

Links

• 🌐 Website: wikilangs.org
• 🤗 Models: huggingface.co/wikilangs
• 📊 Data: wikipedia-monthly
• 👤 Author: Omar Kamali
• 🤝 Sponsor: Featherless AI

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Generated by Wikilangs Models Pipeline

Report Date: 2026-01-10 07:27:32